Intelligent Medical Image Diagnosis Using Deep Learning for Explainable Clinical Decision Support
Download PDFAbstract
Deep learning has demonstrated remarkable success in medical image analysis, often achieving or exceeding human-level performance in various diagnostic tasks. However, the inherent “black-box” nature of these models has been a significant barrier to their widespread adoption in clinical practice, where transparency, trust, and accountability are paramount. This chapter presents a comprehensive framework for an intelligent medical image diagnosis system that integrates a powerful deep learning model with an explainability module to provide transparent and interpretable clinical decision support. We propose a Convolutional Neural Network (CNN) architecture for the classification of chest X-ray images into three categories: Normal, Pneumonia, and COVID-19. To address the black-box problem, we employ Gradientweighted Class Activation Mapping (Grad-CAM) to generate visual explanations that highlight the salient image regions influencing the model’s predictions. Our simulated results on a synthetic dataset demonstrate the high accuracy of the proposed model (92.6%) and the effectiveness of the explainability module in providing clinically relevant insights. The chapter details the complete methodology, from data preprocessing and model design to evaluation and explainability, and discusses the critical role of such systems in augmenting clinical workflows, improving diagnostic confidence, and fostering trust in AI-driven healthcare solutions.
References
- Geert Litjens et al. “A survey on deep learning in medical image analysis”. In: Medical image analysis 42 (2017), pp. 60–88.
- Davide Castelvecchi. “Can we open the black box of AI?” In: Nature News 538.7623 (2016), p. 20.
- Alejandro Barredo Arrieta et al. “Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI”. In: Information fusion 58 (2020), pp. 82–115.
- Varun Gulshan et al. “Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs”. In: jama 316.22 (2016), pp. 2402–2410.
- Andre Esteva et al. “Dermatologist-level classification of skin cancer with deep neural networks”. In: nature 542.7639 (2017), pp. 115–118.
- Amitojdeep Singh, Sourya Sengupta, and Vasudevan Lakshminarayanan. “Explainable deep learning models in medical image analysis”. In: Journal of imaging 6.6 (2020), p. 52.
- Ramprasaath R Selvaraju et al. “Grad-cam: Visual explanations from deep networks via gradient-based localization”. In: Proceedings of the IEEE international conference on computer vision. 2017, pp. 618–626.
- Xiaosong Wang et al. “Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases”. In: IEEE CVPR. Vol. 7. sn. 2017, p. 46.
- Bas HM Van der Velden et al. “Explainable artificial intelligence (XAI) in deep learning-based medical image analysis”. In: Medical image analysis 79 (2022), p. 102470.
Deep Learning: Foundations, Advances, and Intelligent Applications
